Z13 | Macro Paper Warehouse

Digital Distractions with Peer Influence

Mon, 01 Jan 0001 00:00:00 +0000

This paper estimates the causal effects of mobile app usage on college students’ academic performance, physical health, and labor market outcomes, while separately identifying behavioral (endogenous) and contextual (exogenous) peer effects in app usage — the first study to do so within a unified empirical framework. The analysis draws on administrative data for three freshman cohorts (2018–2020) at a mid-tier Chinese university, linked to individual-level mobile phone usage records from a major telecommunications carrier covering 6,430 students over four years (excluding COVID semester). High-frequency GPS data, hourly app usage records for the 2020 cohort, and two waves of university surveys supplement the main dataset.

The identification strategy addresses three challenges: endogeneity of own app usage, endogeneity of peer group formation, and the reflection problem in peer effects. For own usage, two instrumental variables are used: (1) a shift-share instrument interacting the September 2020 launch of the blockbuster game Yuanshen with students’ pre-college app usage intensity; and (2) China’s October 2019 minors’ game restriction policy (prohibiting under-18s from playing online games 10 p.m.–8 a.m. and capping weekday gaming at 90 minutes/day) interacted with the evolving number of underage pre-college friends. For peer effects, the university’s random dormitory assignment within gender-class units provides exogenous peer variation; behavioral peer effects are further isolated using the minors’ restriction policy interacted with roommates’ pre-college underage friend networks, an instrument that affects roommates but not the focal student. Contextual peer effects are recovered by subtracting the estimated behavioral component from reduced-form estimates.

The main findings are as follows. First, app usage is contagious: a one standard deviation (s.d.) increase in roommates’ in-college total app usage raises a student’s own usage by 5.8% (IV). Behavioral peer effects dominate: contextual peer effects are small and statistically insignificant. Second, own app usage severely harms academic performance: a one s.d. increase in total app usage reduces GPA for required courses by 36.2% of a within-cohort-major s.d. (IV), and a one s.d. increase in game app usage alone reduces GPA by 56.6% of a within-cohort-major s.d. The direct disruption effect of roommates’ app usage reduces GPA by a further 20.6% of a within-cohort-major s.d.; combining the indirect channel (behavioral contagion), the total roommate effect reaches 22.7% of a within-cohort-major s.d., more than 60% of the own-usage effect. Third, the effect on physical education scores is roughly four times larger than on required-course GPA: a one s.d. increase in own app usage reduces PE scores by 2.74 points, while roommates’ app usage has no direct effect on PE. Fourth, a one s.d. increase in own in-college app usage reduces initial wages upon graduation by 2.3% (12.1% of within-cohort-major wage s.d.); a one s.d. increase in roommates’ usage reduces wages by 0.9% directly, with a total effect (including the contagion channel) of approximately 1.0% (5.3% of within-cohort-major s.d.). Controlling for cumulative GPA reduces the gaming-to-wage coefficient by roughly one-third, indicating that academic performance is an important but partial mediator.

A back-of-the-envelope policy simulation extending the minors’ gaming cap (3 hours/week) to college students — binding for 34.3% of student-month observations — projects an average wage increase of 0.9% at graduation, approximately half the wage premium from one additional year of work experience in developing countries.

Mechanism evidence from GPS data shows that Yuanshen’s launch caused students to arrive at study halls 18.2 minutes later and leave 23.4 minutes earlier per day. High-frequency sleep data show that a one s.d. increase in nighttime app usage reduces sleep duration by approximately 30 minutes and raises the probability of sleeping late by 34 percentage points. Survey evidence indicates that heavy app users recognize the addictive nature of gaming, pointing to self-control problems rather than lack of awareness.

The scope conditions are: single mid-tier Chinese university; 2018–2020 cohorts; outcomes through initial job placement only; peer group restricted to dormitory roommates; findings rely on IV exclusion restrictions conditional on student and time fixed effects.

Q: What is the core research question? A: The paper asks how individual and peer mobile app usage affect college students’ academic performance, physical health, and early labor market outcomes, and it separately identifies the behavioral (endogenous) versus contextual (exogenous) components of peer influence in app usage. This is claimed as the first study to disentangle these two types of peer effects within a unified empirical framework.

Q: What data does the paper use? A: Administrative records for 7,479 undergraduates across three freshman cohorts (2018–2020) at a medium-sized mid-tier Chinese university are linked to monthly mobile app usage records from a telecommunications provider covering 75% of the provincial population; 6,430 students are matched. The dataset also includes GPS location data at 5-minute intervals, hourly app usage for the 2020 cohort (used to infer sleep), and two waves of voluntary annual surveys with 1,798 respondents (24% response rate). Labor market outcomes — employment status, wages, post-graduate admissions — are available for the 2018 and 2019 cohorts.

Q: How does the paper address the endogeneity of own app usage? A: Two sets of instruments are used. The first interacts the September 2020 launch of Yuanshen (the most popular game in China, with over 13 million Chinese users by 2021, the majority under age 25) with students’ pre-college app usage, forming a shift-share instrument under the assumption that the game launch is orthogonal to unobserved GPA determinants conditional on student fixed effects. The second interacts China’s October 2019 minors’ game restriction policy with the evolving count of a student’s underage pre-college friends; event studies confirm no pre-trends and a sharp, transitory drop in app usage post-policy that dissipates as friends age out of the restricted group.

Q: How does the paper solve the reflection problem and separate behavioral from contextual peer effects? A: Three-step procedure: (1) random dormitory assignment within gender-class units yields reduced-form peer effect estimates using roommates’ pre-college app usage as the exogenous peer shifter; (2) behavioral peer effects are isolated via an IV using the minors’ restriction policy interacted with roommates’ (not the focal student’s) underage pre-college friend networks — an instrument that shifts roommates’ app usage but is orthogonal to the focal student’s outcomes; (3) contextual peer effects are recovered as the residual from subtracting the estimated behavioral effect from the reduced-form estimate.

Q: How large and significant are the behavioral versus contextual peer effects in app usage? A: A one s.d. increase in roommates’ in-college total app usage raises own usage by 5.8% (IV estimate, significant). For game apps alone the behavioral spillover is 10.7%, and for games plus video it is 6.5%. Contextual peer effects (identified from roommates’ pre-college characteristics) are much smaller and statistically insignificant, indicating that peer influence operates primarily through the direct imitation of peers’ actions rather than their background traits.

Q: What is the effect of own app usage on GPA? A: The IV estimate shows a one s.d. increase in total in-college app usage reduces GPA for required courses by 0.716 points, equivalent to 36.2% of a within-cohort-major GPA s.d. (significant at 1%). For game apps alone, a one s.d. increase reduces GPA by 1.119 points, or 56.6% of a within-cohort-major s.d. OLS estimates are biased toward zero, likely because negative health shocks reduce both GPA and app usage simultaneously.

Q: How large is the total peer effect of roommates’ app usage on a student’s GPA? A: Roommates’ app usage directly lowers GPA by 0.408 points (20.6% of within-cohort-major s.d.) through disruption of the dormitory study environment or crowding out of group study. The behavioral contagion channel (5.8% increase in own usage per s.d. of roommates’ usage) adds an additional 0.042 points, bringing the total effect to approximately 0.450 points, or 22.7% of a within-cohort-major s.d. — over 60% of the own-usage effect.

Q: What is the effect on physical education (PE) scores, and why do roommates’ app usage not matter there? A: A one s.d. increase in own total app usage reduces PE scores by 2.74 points (IV), approximately four times the magnitude of the effect on required-course GPA, consistent with health literature on excessive screen time. Roommates’ app usage has no statistically significant direct effect on PE, which the authors attribute to the irrelevance of dormitory noise and study disruptions for outdoor physical activity.

Q: What are the effects of app usage on wages at graduation? A: Doubling total app usage during college reduces initial wages by approximately 2% (IV). A one s.d. increase in own usage reduces wages by 2.3%, or 12.1% of a within-cohort-major wage s.d. A one s.d. increase in roommates’ usage directly reduces wages by 0.9% (4.8% of within-cohort-major s.d.); including the behavioral contagion channel, the total roommate effect is approximately 1.0% (5.3% of within-cohort-major s.d.). Controlling for cumulative GPA reduces the game-usage-to-wage coefficient by about one-third, implying GPA is a partial but not complete mediator.

Q: What does the policy simulation of the gaming cap say? A: Extending the minors’ game restriction (3 hours/week cap) to college students would bind for 34.3% of student-month observations, reducing average monthly gaming from 12.1 hours to 8 hours (a one-third decrease). Incorporating the behavioral peer multiplier for gaming (0.078), average gaming further converges to approximately 7.65 hours in steady state. The implied wage gain at graduation is 0.9%, approximately half the wage premium from one additional year of work experience in developing countries (Lagakos et al., 2019 estimate).

Q: What does the GPS evidence show about time allocation? A: Following Yuanshen’s launch, the average student arrives at the study hall 18.2 minutes later and returns to the dormitory 23.4 minutes earlier per day. The minors’ restriction reverses this: students with the average number of minor friends arrive at study halls 17.4 minutes earlier and return to the dorm 19.8 minutes later. Both game shocks also shift tardiness and absence rates for major-required courses in the expected directions, and the effects intensify over time with Yuanshen’s growing popularity.

Q: What do the sleep data show? A: A one s.d. increase in nighttime app usage (9 p.m.–3 a.m.) is associated with roughly 30 minutes less sleep (7% of the mean), a 34 percentage point higher probability of sleeping late, and a 4.5 percentage point higher probability of waking up late. Daytime app usage (8 a.m.–9 p.m.) is also associated with 7.2 fewer minutes of sleep (1.8% of mean) and a 3.7 percentage point higher probability of late wake-up. These results are descriptive (from the 2020 cohort hourly data) rather than IV-based.

Q: What does the survey evidence show about mechanisms and self-awareness? A: Heavier app users report worse physical health and higher stress, are less likely to have obtained professional certifications by graduation, submit fewer job applications, and express lower satisfaction with job offers. Notably, heavier users are more likely to acknowledge the addictive nature of apps and games, suggesting a self-control problem rather than informational deficiency. They also report better relationships with roommates and greater likelihood of following roommates’ advice on post-graduation choices, a potential direct channel for peer labor market effects.

Q: How representative is the sample, and what are the key scope conditions? A: The university is a mid-tier institution in southern China with students predominantly from the 30th–80th CEE score percentile among provincial college-admitted applicants; it is less female (42% vs. 53% nationally) and more rural (40% vs. 27% nationally). Survey respondents oversample less advantaged backgrounds and are re-weighted. Findings pertain to dormitory roommates as the peer group; all labor market outcomes are initial wages upon graduation; the sample covers 2018–2021 with COVID semester excluded. The peer effects estimates rest on random dormitory assignment, which the authors verify by showing no within-dorm correlation in pre-college characteristics.

Behavioral (endogenous) peer effects: The mechanism by which a peer’s actual behavior — here, contemporaneous app usage — directly influences a focal individual’s own behavior. In this paper, identified via IV using the minors’ game restriction policy interacted with roommates’ underage pre-college friend networks, which shifts roommates’ usage but not the focal student’s characteristics.

Contextual (exogenous) peer effects: The influence of peers’ pre-determined background characteristics (e.g., pre-college app usage, reflecting motivation, study habits, attitudes toward academics) on a focal individual’s outcomes, independent of peers’ actual in-college behavior. Recovered as the residual after subtracting estimated behavioral peer effects from reduced-form estimates; found to be small and insignificant in this setting.

Shift-share instrument (Yuanshen): A quasi-experimental instrument constructed by interacting the mid-sample launch date of the blockbuster game Yuanshen (September 2020) with students’ pre-college app usage intensity, under the assumption that pre-college usage predicts differential susceptibility to the shock while the launch itself is orthogonal to the university’s academic environment.

Minors’ game restriction policy: China’s October 2019 policy prohibiting individuals under 18 from playing online games between 10 p.m. and 8 a.m. and capping weekday gaming at 90 minutes per day (tightened to 3 hours/week in September 2021). Used both as an instrument for own app usage (via underage pre-college friends) and as an instrument for roommates’ usage (via roommates’ underage friends) to isolate behavioral peer effects.

Reflection problem: The identification challenge first articulated by Manski (1993) arising because an individual’s behavior both affects and is affected by peers simultaneously, making it impossible to separately identify the direction of influence from observational data without exogenous variation in peer behavior.

Source text origin: The paper’s own data provenance category distinguishing whether summaries are based on full working paper text (pdf or oa-html) versus abstract only — a distinction the paper itself does not use but that is relevant to the review pipeline running this analysis.

Within-cohort-major GPA standard deviation: The unit used to scale all GPA effect sizes, defined as the standard deviation of GPA within students of the same graduation cohort and declared major. This normalization accounts for systematic differences in grading across fields and years, making effect magnitudes comparable across specifications.

Silence to Solidarity: How Communication About a Minority Affects Discrimination

Mon, 01 Jan 0001 00:00:00 +0000

This paper examines how two types of communication about a minority group affect discriminatory behavior: (i) horizontal communication between majority-group members, and (ii) top-down communication from agents of authority such as the legal system. The setting is urban Chennai, India, where the paper measures discrimination against thirunangai — a community of transgender women who are India’s most visible LGBTQ+ group — in a field experiment with 3,397 participants.

Discrimination is measured using incentivized hiring choices. Participants are offered a free grocery delivery and make 10 binary choices over which worker will carry out the delivery, with worker gender (cisgender male, cisgender female, or transgender) varying across options. The stakes are real: one choice is randomly selected and implemented 2–9 weeks later. Participants in the control condition are highly discriminatory: they are 19 percentage points (32%) less likely to hire a transgender worker than a non-transgender worker (p<0.001), and are willing to sacrifice grocery items worth 1.9 times their median daily per capita food expenditure to avoid a 15-minute interaction with a transgender worker.

The first main treatment involves randomly assigning participants to a 3-person group discussion with two neighbors, in which they discuss and make collective hiring choices over the same options. The key outcome is participants’ subsequent private, individual hiring choices. The discussion eliminates anti-transgender discrimination on average: participants in the discussion arm are 17 percentage points (42%) more likely to select a transgender worker in their private post-discussion choices relative to the control group (p<0.001), so that discrimination is no longer statistically distinguishable from zero (p=0.30). The discussion’s effect is partially persistent: approximately one month later, discussion participants are still 4 percentage points more likely to select transgender workers in hypothetical hiring choices (p=0.03), representing roughly 25% of the short-run effect.

The second main treatment cross-randomizes a video shown before hiring choices. The legal rights video informs participants of a Supreme Court ruling affirming that transgender people hold the same fundamental constitutional rights as other citizens. This reduces discrimination by 10.3 percentage points (p<0.001). A rights messaging video — which argues that transgender people should have equal rights without invoking legal authority — reduces discrimination by a smaller 5.8 percentage points (p=0.001), and there is some evidence the legal-authority version is more effective (p of difference in [0.01, 0.12]). However, the legal rights video’s effect is only 59% as large as the discussion’s effect (p of difference in [0.002, 0.04]), and it does not persist at the one-month follow-up (p in [0.12, 0.51]).

The paper rules out two candidate mechanisms for the discussion’s effects and supports a third. First, the discussion does not work primarily through correcting misperceived norms: while control-group participants do overestimate peer discrimination by 5 percentage points, the discussion reduces predicted discrimination by 24 percentage points — far more than a corrected misperception could explain (at most 21% of the effect under generous assumptions). Second, the discussion does not work through virtue signaling alone: a “No discussion (public)” arm in which participants make individually-visible choices shows no reduction in discrimination on average (p=0.83). Third, the paper provides affirmative evidence for a persuasion channel: participants in a “listener” arm, who silently observe a 2-person discussion without participating, discriminate 13 percentage points less than the control group (p<0.001), an effect that is highly persistent at the 2–9 week follow-up (11 percentage points, p<0.001). The persuasion mechanism is further supported by the finding that pro-trans participants are more vocal: each additional transgender worker chosen in post-discussion private choices is associated with a 32% higher probability of speaking first (p=0.03) and a 27% higher probability of dominating the discussion (p=0.02). Statements about transgender workers during discussions were 5.7 times more likely to be positive than negative. Listeners who heard moral argumentation about equality, rights, and giving opportunities subsequently discriminated less (p<0.001).

Scope conditions: the study is conducted among urban Chennai residents (85% female), where transgender identity is visually recognizable and socially salient, awareness of the 2014 Supreme Court ruling is low (36% could not identify a single legal right transgender people hold), and a wedge exists between descriptive norms (high actual discrimination) and prescriptive norms (93% of the control group rate explicit discrimination as wrong). The model’s “sweet spot” logic implies these effects may not generalize to settings where discrimination is either near-universal (no privately pro-trans individuals to be vocal) or already minimal (no incentive to persuade).

Q: How is anti-transgender discrimination measured in the experiment? A: Participants make 10 incentive-compatible binary hiring choices over grocery delivery workers, with one choice randomly selected and implemented 2–9 weeks later. Discrimination is defined as the reduction in the probability of selecting the alternative worker when that worker is transgender versus non-transgender, conditional on other option characteristics such as items offered and reliability score. Participants are told they will have a 15-minute conversation with the selected worker, ensuring anticipated social contact. The design is framed as market research to obfuscate the study’s purpose; only 8% correctly guessed the true focus.

Q: How large is baseline discrimination in the control group? A: In the No discussion (private) control condition, participants are 19 percentage points (32%) less likely to hire a transgender worker than a non-transgender worker (p<0.001). In willingness-to-pay terms, participants sacrifice grocery items worth 1.9 times their median daily per capita food expenditure (Rs. 127 on a base of Rs. 67) to avoid selecting a transgender worker. Even when a transgender worker dominates on both items and reliability score, participants in the control group still select the non-transgender worker 47% of the time.

Q: What is the main effect of the 3-person group discussion on subsequent discrimination? A: Participants who engage in a group discussion with two neighbors are 17 percentage points more likely to select a transgender worker in their subsequent private individual choices (p<0.001). This eliminates average discrimination entirely: in the discussion arm, the probability of selecting a transgender worker is not statistically distinguishable from the probability of selecting a non-transgender worker (p=0.30). The willingness-to-pay to avoid a transgender worker falls from Rs. 127 to Rs. 13 (p of difference < 0.001), and is no longer significantly different from zero (p=0.265).

Q: How persistent are the effects of the group discussion? A: At the 2–9 week follow-up survey (mean 35 days), discussion participants are approximately 4 percentage points more likely to select transgender workers in hypothetical hiring choices (p=0.03). This represents approximately 25% of the short-run 17 percentage point effect, a decay rate comparable to the persistence of US political advertising effects in the political science literature (Hill et al., 2013, estimate 10–15% remaining after 30 days).

Q: What is the effect of the legal rights video, and how does it compare to the discussion? A: The legal rights video — informing participants of the Supreme Court ruling affirming transgender people’s fundamental constitutional rights — increases the probability of selecting a transgender worker by 10.3 percentage points (p<0.001). The rights messaging video, which argues that transgender people should have equal rights without invoking legal authority, increases it by 5.8 percentage points (p=0.001). The legal rights video’s effect is only 59% as large as the discussion’s 17 percentage point effect (p of difference in [0.002, 0.04]), and unlike the discussion, neither video’s effect is detectable at the one-month follow-up (p in [0.12, 0.51]).

Q: Does the legal rights video work through a different channel than the rights messaging video? A: There is evidence that the legal authority of the Supreme Court matters beyond the content of the rights message. The legal rights video is more effective than the rights messaging video at reducing discrimination (p of difference in [0.01, 0.12]), and the legal rights video (but not the rights messaging) affects participants’ beliefs about the legal status of transgender people (as measured by a summary index). Both videos shift perceived descriptive norms — participants predict others will select transgender workers more, by 2–6 percentage points — but neither significantly affects attitudes as measured by a list experiment or disapproval questions.

Q: Does the discussion work through correcting misperceived norms? A: This channel can account for at most a small fraction of the effect. Control-group participants do overestimate peer discrimination by 5 percentage points in incentivized predictions (p<0.001, as measured by predicted probability of selecting a transgender worker). However, the discussion reduces predicted discrimination by 24 percentage points (p<0.001), far exceeding the initial misperception. Even under generous assumptions in which the misperception is precisely corrected, this mechanism could account for no more than 21% of the discussion’s treatment effect (95% CI: [8.9%, 32.5%]).

Q: Does the discussion work through virtue signaling? A: The evidence rules out virtue signaling as the primary channel. The “No discussion (public)” treatment arm makes participants’ individual hiring choices visible to their group members, exogenously increasing social image concerns in the absence of a discussion. This has no detectable average effect on discrimination (p=0.83), indicating that social image concerns alone — without the persuasive content of an actual discussion — do not explain the reduction in discrimination generated by the group discussion.

Q: What is the evidence for the persuasion mechanism? A: The “listener” treatment arm provides direct evidence. In this arm, one participant silently observes a 2-person discussion without speaking, then makes private individual choices. Listeners discriminate 13 percentage points less than the control group (p<0.001), an effect statistically indistinguishable from full discussion participants. Since listeners changed their behavior based solely on what they heard and saw, this constitutes evidence of persuasion. The listener effect is highly persistent at the 2–9 week follow-up (11 percentage points, p<0.001) and holds on a robustness outcome designed to be completely private. The implied persuasion rate is 29%, described as high relative to values in the literature (DellaVigna & Gentzkow, 2010).

Q: Why do pro-trans participants persuade others — what drives the discussion’s content? A: Pro-trans participants are disproportionately vocal. Each additional transgender worker chosen in post-discussion private choices (a proxy for pro-trans private attitudes) is associated with a 32% higher probability of speaking first (p=0.03) and a 27% higher probability of dominating the discussion (p=0.02), but only when discussing a choice involving a transgender worker. The overall tone of discussions is strongly pro-trans: statements about transgender workers are 5.7 times more likely to be positive than negative. Participants who hear moral argumentation about equality, rights, and giving opportunities subsequently discriminate significantly less (p<0.001).

Q: Does the discussion work by changing statistical (belief-based) discrimination? A: Partially, baseline discrimination in the control group is partly statistical: despite transgender workers having the same average reliability scores as others, participants rate them as less likely to complete a delivery, and revealing the true reliability score makes participants 2.9 percentage points more likely to select a transgender worker (an effect unique to transgender workers). However, the discussion does not significantly affect beliefs about transgender workers’ reliability, and there is no detected reduction in the belief-based component of discrimination in the discussion arm (though the test is underpowered).

Q: Are the effects of the discussion and the legal rights video additive? A: The two interventions appear to combine approximately linearly for the legal rights video: there are no detected interaction effects (p in [0.83, 0.96]). By contrast, there is weak evidence of a negative interaction between the rights messaging video and the discussion, suggesting these two may be substitutes — consistent with the rights messaging video’s content being similar to the pro-trans moral argumentation already present in discussions.

Q: What alternative explanations are ruled out? A: The paper tests and finds no support for: (i) photo characteristics such as perceived caste driving results; (ii) social image concerns affecting even post-discussion private choices (the “extra private” robustness outcome designed to be unobservable by neighbors yields similar results); (iii) increased contemplation or deliberation about choices; (iv) experimenter demand effects or social desirability bias (treatment effects do not differ for the 8% who guessed the study’s purpose); (v) increased salience of the transgender category; and (vi) cheap talk from low stakes (choices were incentive-compatible and implemented).

Q: What is the study’s theoretical model for why pro-trans participants speak out? A: The paper develops a model combining social signaling (people want to fit in with their group; Bénabou & Tirole, 2006) with direct persuasion (participants can change each other’s preferences through messages). Under the right conditions, only pro-trans participants send persuasive pro-trans messages. This occurs in a “sweet spot” range: when average discrimination is not so strong that no one is privately pro-trans, and not so weak that pro-trans participants lack an incentive to persuade (since they are already in the majority). The context in Chennai — high actual discrimination but strong social norms against it — satisfies this sweet spot condition.

Q: What are the policy implications regarding horizontal versus top-down communication? A: In this context, facilitating horizontal communication between neighbors is a more effective tool for reducing discrimination than top-down communication about legal rights: the discussion’s effect is 1.7 times larger than the legal rights video (17 p.p. vs. 10.3 p.p.) and partially persists at one month, whereas the legal rights video’s effect does not persist. However, the legal rights video does reduce discrimination relative to the rights messaging video, suggesting that communicating the legal authority of the Supreme Court carries independent weight beyond rights advocacy messaging. Both interventions are complementary when combined.

Horizontal communication: Communication between members of the majority group about a minority, as distinct from contact between majority and minority groups or top-down communication from authority. In this paper, operationalized as a group discussion among three neighbors who make collective hiring choices.

Top-down communication: Communication from agents of authority — here, the legal system — about a minority group’s rights. Measured via a video informing participants of a Supreme Court ruling affirming transgender people’s constitutional rights.

Anti-transgender discrimination: In the paper’s own measurement, the reduction in the probability that a worker is chosen because they are transgender (relative to being non-transgender), conditional on other delivery option characteristics. Measured in incentivized, privately-elicited binary hiring choices.

Expressive law hypothesis: The theory that changes in the law affect behavior by changing people’s perception of the prevailing social norm, not (only) through deterrence. The paper tests this by comparing a legal rights video (invoking Supreme Court authority) to a rights messaging video with identical content but no legal backing, finding the legal-authority version more effective.

Persuasion channel: The mechanism by which discussion participants change each other’s preferences through persuasive messages, particularly moral arguments about equality and rights. Distinguished in the paper from virtue signaling (publicly visible pro-trans behavior) and norm correction (updating misperceived beliefs about peer behavior).

Pluralistic ignorance: A setting in which people misperceive how common discriminatory attitudes are among their peers, potentially hiding genuine minority support for the discriminated group. The paper tests this as a candidate mechanism and finds it can account for at most 21% of the discussion effect.

Sweet spot condition: The range of average group discrimination levels in which pro-trans participants have both the motivation and opportunity to speak out persuasively — discrimination is not so universal that no one is privately pro-trans, and not so minimal that the pro-trans participants feel no need to persuade others. The paper argues the Chennai context satisfies this condition.

Trust and Innovation Within the Firm

Mon, 01 Jan 0001 00:00:00 +0000

This paper investigates whether and how a CEO’s inherited generalized trust enhances innovation within firms, offering a micro-foundation for the well-documented macro-level relationship between societal trust and economic growth. The author argues that trust — by inducing tolerance of failure — encourages researchers to undertake high-risk, explorative R&D rather than safe exploitation of known approaches.

The empirical foundation is a matched CEO-firm-patent dataset covering 5,753 CEOs at 3,598 US public firms during 2000–2011, encompassing 700,000 patents and over one million inventors. CEO trust is measured as an inherited trait: each CEO’s ethnic origin is inferred probabilistically from their last name using de-anonymized US censuses from 1910–1940, and ethnic-origin-specific trust levels are drawn from the US General Social Survey (GSS), restricted to respondents in highly prestigious occupations. The resulting trust measure is the weighted average of ethnic-specific trust scores across a CEO’s likely ethnic composition.

The main empirical strategy exploits within-firm variation across CEO transitions, using firm and year fixed effects to compare patenting before and after a CEO change. The identifying assumption — that the timing of CEO transitions and the new CEO’s trust level are not predicted by prior firm patenting trends — is supported by event-study tests showing flat pre-trends. A one-standard-deviation increase in CEO inherited generalized trust (equivalent to the difference between Greek and English averages) is associated with a 6.2–6.3% increase in patent filings, statistically significant at the 1% level. For the average firm, this equals approximately 1.1 additional patents annually, worth roughly $6.8 million. The effect is larger among exogenous transitions (CEO retirement or death): 8.5% in the restricted sample, and an IV estimate of 8.2%. The back-of-envelope calculation suggests this trust-innovation channel could account for approximately 37% (range: 16–58%) of the effect of trust on GDP per capita growth.

The paper’s central mechanism — risk taking — is tested by examining the distribution of patent quality rather than the mean. Under the risk-taking mechanism, trust should increase the variance of R&D project quality, raising high-quality patents without necessarily increasing low-quality ones. Consistent with this, CEO trust raises only above-median quality patents (measured by forward citation decile), with effects increasing monotonically toward the top decile and no statistically significant effect on below-median patents. Average patent quality as measured by citation-weighted counts or patent value rises by 4–6%. Trust also disproportionately raises the share of explorative patents (those with at least 90% of backward citations outside the firm’s existing knowledge stock) by 1 percentage point over a base of 17%.

The transmission channel is examined using BERT-based classification of nearly one million Glassdoor employee reviews. Under more trusting CEOs, firms exhibit stronger top-down trust sentiment (managers trusting workers), particularly among R&D workers and scientists. The effect materializes within the first two years of a CEO term. Director selection provides an additional transmission mechanism: under more trusting CEOs, newly appointed directors are more trusting and departing directors are less trusting.

A within-CEO design using bilateral trust (toward researchers in specific countries) with CEO fixed effects addresses omitted CEO characteristics. A one-standard-deviation increase in CEO bilateral trust toward a country is associated with a 5% increase in patents by inventors in that country’s R&D lab, controlling for firm-by-year, CEO, and inventor-country fixed effects.

The effect is strongest when CEO trust is matched to a high-quality researcher pool; in firms with mostly low-quality researchers, high trust may be counterproductive. Trust is also a substitute for R&D knowledge: the effect disappears when the CEO holds a non-MBA graduate degree or has prior R&D experience.

Q: What is the main research question? A: The paper asks whether a CEO’s generalized trust causes more and higher-quality innovation within the firm, and through what mechanism. It also asks how trust transmits from the CEO to researchers who rarely interact with the CEO directly.

Q: How is CEO trust measured? A: CEO trust is measured as an inherited trait using a two-step procedure. First, each CEO’s last name is probabilistically mapped to one or more ethnic origins using four de-anonymized US censuses (1910–1940). Second, ethnic-origin-specific trust is computed from GSS respondents in highly prestigious occupations. The CEO’s trust measure is the weighted average across ethnic compositions. This measure is shown to be more precise than an individual-level survey measure and approximately 80% as precise as a game-based measure, without introducing attenuation bias.

Q: What is the baseline patent effect and how large is it economically? A: A one-standard-deviation increase in CEO inherited trust is associated with a 6.2–6.3% increase in patent filings (statistically significant at 1%). For the average baseline firm, this is approximately 1.1 additional patents per year, valued at roughly $6.8 million. When patent quality is accounted for, the effect rises to 9.9% using citation-weighted patent count and 11.5% using patent value based on excess stock returns on grant dates.

Q: Is the effect causal? What identification strategy is used? A: The main strategy uses firm and year fixed effects, identifying the effect from within-firm variation around CEO transitions. Pre-trend tests confirm that neither the timing of CEO changes nor the new CEO’s trust level predicts prior firm patenting. Among exogenous transitions (CEO retirements and deaths), the effect is 8.5%, and an IV estimate using the predecessor’s trust as instrument yields 8.2% (significant at 10%), both comparable to the baseline.

Q: What is the macroeconomic significance of the trust-innovation channel? A: Combining the paper’s trust-to-patents estimate (0.042–0.062) with Akcigit et al.’s (2017) patents-to-GDP-growth estimate (0.026–0.066) and the cross-country trust-to-growth coefficient (0.007), the trust-innovation channel could explain approximately 37% of the effect of trust on growth, with a plausible range of 16–58%.

Q: What is the mechanism linking CEO trust to innovation? A: The conceptual mechanism is that a more trusting manager interprets researcher failure as bad luck rather than bad type, making her more likely to tolerate failure and continue employing the researcher. This increases the researcher’s incentive to pursue explorative, high-risk R&D over safe exploitation of known approaches. The mechanism implies a variance-increasing effect on the R&D quality distribution, rather than a mean shift.

Q: How is the risk-taking mechanism tested against alternative mechanisms? A: The paper examines the distribution of patent quality by citation decile. Under mean-shifting alternatives (delegation, cooperation, relational contracting), trust should raise all quality brackets. Under risk-taking, trust raises only high-quality patents. The results show CEO trust has monotonically increasing effects from low to high quality deciles, with no statistically significant effect on below-median patents, consistent only with the variance-increasing (risk-taking) mechanism.

Q: What patent quality measures are used and what do they show? A: Beyond forward citation deciles, the paper uses explorativeness (patents with at least 90% of backward citations outside the firm’s existing knowledge stock), disruptiveness (Funk and Owen-Smith, 2017), patent importance (Kelly et al., 2021), backward citations to scientific literature, and patent scope. Trust increases all these measures with statistically significant positive coefficients. The share of explorative patents rises by 1 percentage point over a base of 17%. Average citation count and patent value increase by 4–6%.

Q: Does CEO trust raise R&D expenditure? A: No. The coefficients from regressing R&D expenditure on CEO trust are neither statistically significant nor large enough to explain the innovation effect. The patent effect is also robust to controlling for R&D inputs, suggesting that trust affects the type of projects chosen (consistent with risk-taking) or their realized outcomes, rather than the scale of R&D.

Q: How does CEO trust transmit to corporate culture? A: Using BERT-based classification of nearly one million Glassdoor reviews covering 266 firms and 397 CEO terms between 2008 and 2017, the paper finds that CEO trust is associated with stronger top-down trust sentiment (managers trusting workers). The normalized effect of a one-standard-deviation increase in CEO trust on overall trust sentiment is 0.257, on top-down trust 0.531, and on bottom-up trust only 0.141 (statistically insignificant). The effect is strongest among reviewers who identify as scientists, researchers, or engineers, and materializes within the first two years of the CEO term.

Q: What evidence exists for transmission via director selection? A: Under more trusting CEOs, newly appointed directors — especially those who remain until the end of the CEO term — are more trusting, and departing directors are less trusting. The average director trust improves during the CEO’s term. Because 54% of director hirings and 46% of turnovers occur within the first two years, this change also materializes quickly, consistent with the dynamic pattern of trust culture change.

Q: What is the within-CEO bilateral trust result and what does it add? A: Using within-CEO variation in bilateral trust toward researchers from different countries (from Eurobarometer surveys), and controlling for CEO, inventor-country, and firm-by-year fixed effects, a one-standard-deviation increase in CEO bilateral trust toward a country is associated with a 5% increase in patents by inventors in that country’s R&D lab. This design allows CEO fixed effects, ruling out unobserved CEO-level confounders such as management style or R&D ability.

Q: When is CEO trust counterproductive? A: CEO trust is beneficial only when matched to a high-quality researcher environment. Using residual patent output (controlling for observable firm and CEO characteristics) as a proxy for researcher quality, the effect of CEO trust on patents, patent output per R&D dollar, and future sales/employment/TFP is significant only among firms in the top two quintiles of researcher quality. In firms with mostly low-quality researchers, high CEO trust may be counterproductive by failing to screen out bad researchers.

Q: How does the trust effect vary by industry and CEO background? A: The effect is ubiquitous across industries but especially pronounced in pharmaceutical and ICT firms. The timing varies: it manifests quickly in ICT (short R&D lag) and more slowly in pharma (long R&D horizon). The effect vanishes when the CEO holds a non-MBA graduate degree or has prior R&D experience, suggesting trust is a substitute for direct knowledge of R&D processes.

Q: Are the results robust? A: Yes. The paper reports 14 categories of robustness checks including alternative patent transformations, alternative trust measures (LASSO, World Value Survey, Global Preference Survey, alternative GSS questions), alternative standard error clustering, Poisson count models, restriction to granted patents, exogenous transition subsamples, modern difference-in-differences estimators (de Chaisemartin et al., 2024; Sun and Abraham, 2021; Callaway and Sant’Anna, 2021; Borusyak et al., 2024), and leave-one-ethnicity-out. The baseline result is stable across all these checks.

Inherited generalized trust: The paper’s measure of a CEO’s trust disposition, defined as the probability-weighted average of ethnic-origin-specific trust levels (from the GSS) based on the CEO’s likely ethnic composition inferred from their last name and historical census records. It captures the culturally transmitted component of trust, distinct from individual-level noise.

Explorative R&D: In the paper’s framework (building on March, 1991), research activities that involve testing untested paths, carrying high risk of failure but high potential for innovation, as opposed to exploitation of well-known approaches with low failure risk. The paper argues CEO trust encourages researchers to shift toward exploration.

Tolerance of failure: A manager’s propensity to attribute a researcher’s failure to bad luck rather than bad type. Under the paper’s mechanism, a more trusting manager gives greater weight to bad luck, making her more likely to retain the researcher after failure, thereby incentivizing risk taking.

Top-down trust: In the paper’s BERT-based classification of Glassdoor reviews, the direction of trust from managers toward workers (as opposed to bottom-up trust from workers toward managers). The paper finds CEO trust primarily raises top-down trust sentiment, especially among R&D workers.

Patent explorativeness: A patent quality measure defined as the share of its backward citations that fall outside the firm’s existing knowledge stock; patents are classified as explorative if at least 90% of backward citations are outside that stock. The paper uses this as a direct measure of explorative R&D output.

Bilateral trust: CEO d’s directed trust toward individuals from country c, computed analogously to inherited generalized trust but using Eurobarometer survey data on country-pair trust attitudes among European-origin populations. Used in the within-CEO design to control for CEO fixed effects.

Variance-increasing mechanism: The paper’s characterization of the risk-taking channel, in which CEO trust raises the variance (not the mean) of the R&D project quality distribution by encouraging researchers to pursue high-risk, high-reward exploration. Empirically identified by the pattern that trust raises only above-median quality patents with monotonically increasing effects toward the top decile.